NEWS 2020-03-30:
Examples Linear and Logarithmic Scale
Confirmed Cases: USA
Confirmed Cases: Germany
Latest Per Capita Confirmed Cases and Deaths by Country
Latest per Capita Counts
Selected Countries Plots
Confirmed Cases: Country Comparison
Deaths: Country Comparison
Selected U.S. States Plots
Confirmed Cases: U.S. States Comparison
Deaths: U.S States Comparison
Other Plots
Mortality
3-day Change in Deaths
3-day Change in Confirmed Cases
NEW 2020-03-29: Doubling Rate in Days: Confirmed Cases
NEW 2020-03-29: Doubling Rate in Days: Deaths
NEW 2020-03-30: Doubling Rate in Days: Confirmed Cases (U.S.
NEW 2020-03-30: Doubling Rate in Days: Deaths (U.S)
Suggested Reading
Data Sources
NOTE: Move cursor over the plots to see actual data points
Josef Kellndorfer, Ph.D., Earth Big Data, LLC, Richard Signell, Ph.D., USGS
These plots show the daily status of COVID-19 cases as reported by Johns Hopkins University. Please use freely to look at daily changes and trends. Keep in mind that data are changing frequently as more Covid-19 tests become available globally. We chose to plot totals and numbers normalized by population (expressed as per 100,000). Also, it is advantageous to plot case totals (confirmed infections, deaths, and recovered) in logarithmic scale where trends and parallels between countries become more obvious. Note, that a straight line trending upwards in logarithmic scale indicates exponential increase! Taking a close look at the plots, one will discern differences and similarities, and that for the most part initial stages are similar in all countries with a time lag. What to look out for is whether the measures taken by countries, foremost social distancing show the desired effects of slowing and eventually reversing the exponential upwards trends. The first set of plots looks at confirmed infections, the bottom set of plots looks at confirmed deaths, which may be somewhat more reliable with respect to an impact for a country while tests are rolled out in larger numbers. We also plot mortality (actual numbers and over time), and 3-day comparison of confirmend cases and deaths.
This is a work in progress, stay tuned.
You can get the notebook underlying this work at: https://github.com/EarthBigData/covid19
For questions: Please use the email webform at earthbigdata.com
You can use the control buttons to interact with the plots, e.g. zoom in/out or also hover over the data points to get a detailed number.
Click on a label in the legend to dim/highlight a specific country or state.
The plot below shows the mortality rate in percent computed as:
$Mortality=\frac{Deaths}{Infected} * 100$
Two caveats:
The plots below show the change of total number of deaths compared to three days before the plotted date. A factor 2 means the cases doubled after three days. A factor 1 means no new deaths are reported compared to three days before. (Plots also inspired by Jennifer Bardwell, Jim Bardwell).
The plots below show the change of total number of confirmed cases compared to three days before the plotted date. A factor 2 means the cases doubled after three days. A factor 1 means no new confirmed cases are reported compared to three days before. (Plots also inspired by Jennifer Bardwell, Jim Bardwell).
The plots below show the change rate (${doubling.rate}_{confirmed.cases}$) in number of days for confirmed cases to double ($days_{confirmed.cases.double}$). This is expressed as
${doubling.rate}_{confirmed.cases} = \frac{1}{days_{confirmed.cases.double}}$
In this representation a factor of 1 means cases double every day, 0.5 means cases double every 2nd day, 0.33 means cases double every third daty, 0.25 menas cases double every 4th day, etc. When the line approaches 0, no more cases are identified.
Plots begin at more than 100 confirmed cases.
(Plots inspired by Jennifer Bardwell, Jim Bardwell).
The plots below show the change rate (${doubling.rate}_{deaths}$) in number of days for deaths to double ($days_{deaths.double}$). This is expressed as
${doubling.rate}_{deaths} = \frac{1}{days_{deaths.double}}$
In this representation a factor of 1 means death counts double every day, 0.5 means death counts double every 2nd day, 0.33 means death counts double every third day, 0.25 menas death counts double every 4th day, etc. When the line approaches 0, no more deaths are counted.
Plots begin at more than 25 deaths.
(Plots inspired by Jennifer Bardwell, Jim Bardwell).
The plots below show the change rate (${doubling.rate}_{confirmed.cases}$) in number of days for confirmed cases to double ($days_{confirmed.cases.double}$). This is expressed as
${doubling.rate}_{confirmed.cases} = \frac{1}{days_{confirmed.cases.double}}$
In this representation a factor of 1 means cases double every day, 0.5 means cases double every 2nd day, 0.33 means cases double every third daty, 0.25 menas cases double every 4th day, etc. When the line approaches 0, no more cases are identified.
Plots begin at more than 100 confirmed cases.
(Plots inspired by Jennifer Bardwell, Jim Bardwell).
The plots below show the change rate (${doubling.rate}_{deaths}$) in number of days for deaths to double ($days_{deaths.double}$). This is expressed as
${doubling.rate}_{deaths} = \frac{1}{days_{deaths.double}}$
In this representation a factor of 1 means death counts double every day, 0.5 means death counts double every 2nd day, 0.33 means death counts double every third day, 0.25 menas death counts double every 4th day, etc. When the line approaches 0, no more deaths are counted.
Plots begin at more than 10 deaths.
(Plots inspired by Jennifer Bardwell, Jim Bardwell).
Coronavirus Interview with Larry Brilliant
Brookings Institute: A mortality perspective on COVID-19: Time, location, and age
COVID-19 confirmed cases, deaths and recovered cases data are streamed from the The Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. The CCSE COVID-19 GitHub Repo has more information about these data and their sources.
Since Johns Hopkins changed the data format on 2020-03-23, they do not provide the US compiled time series data yet. We acknowldege Sooth Sawyer who helped out by compiling the data set in the old format at: https://www.soothsawyer.com
We obtain the Population data from UN statistics. UN Population Data Sets have more information about these data and their sources.
US population data ar obtained from US Census statistics. US Population Data Sets have more information about these data and their sources.